OpenAI introduced a new evaluation suite and research paper on Chain-of-Thought controllability. The company says GPT-5.4 Thinking shows low ability to obscure its reasoning, which supports continued use of CoT monitoring as a safety signal.
A new llama.cpp change turns <code>--reasoning-budget</code> into a real sampler-side limit instead of a template stub. The LocalLLaMA thread focused on the tradeoff between cutting long think loops and preserving answer quality, especially for local Qwen 3.5 deployments.
OpenAI released proof attempts for all 10 First Proof problems and said expert feedback suggests at least five may be correct. The company positioned the result as a test of long-horizon reasoning beyond standard benchmarks.
A well-received HN post highlighted Sarvam AI’s decision to open-source Sarvam 30B and 105B, two reasoning-focused MoE models trained in India under the IndiaAI mission. The announcement matters because it pairs open weights with concrete product deployment, inference optimization, and unusually strong Indian-language benchmarks.
Google AI Developers announced that Gemini 3.1 Flash-Lite is rolling out in preview via the Gemini API and Google AI Studio. The post positions it as the fastest and most cost-efficient model in the Gemini 3 line, now adding dynamic thinking for task-adaptive reasoning.
Anthropic's Claude Opus 4.6 independently solved a directed Hamiltonian cycle decomposition problem that computer science legend Donald Knuth had spent weeks working on. Knuth documented the achievement in a formal Stanford paper, marking one of the first times a top-tier computer scientist has formally credited an LLM with solving a genuine research problem.
A counterintuitive study found that programming AI agents with more assertive, 'rude' conversational behaviors — including interrupting and strategic silence — significantly improved their performance on complex reasoning tasks.
Google DeepMind announced Gemini 3.1 Pro on February 19, 2026 as an upgraded core model for harder tasks. The company highlighted a verified 77.1% score on ARC-AGI-2 and broad rollout across developer, enterprise, and consumer surfaces.
Opper tested 53 leading LLMs with a deceptively simple logic question about whether to walk or drive to a car wash 50 meters away. Only 11 models answered correctly — the car must be driven to the car wash.
Opper tested 53 leading LLMs with a deceptively simple logic question about whether to walk or drive to a car wash 50 meters away. Only 11 models answered correctly — the car must be driven to the car wash.
Google's Gemini 3.1 Pro achieves 77.1% on ARC-AGI-2—more than doubling the previous Gemini 3 Pro's score. The mid-cycle upgrade brings Deep Think-level reasoning capabilities to all users and developers.
Google DeepMind has released Gemini 3.1 Pro with over 2x reasoning performance versus Gemini 3 Pro. The model scores 77.1% on ARC-AGI-2 (up from 31.1%), 80.6% on SWE-bench Verified, and tops 12 of 18 tracked benchmarks at unchanged $2/$12 per million token pricing.